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Despite the proven benefits of natural ventilation (NV) as an effective low-carbon solution to meet growing cooling demand, its effectiveness can be constrained by poor outdoor air quality. Here, we propose a modeling approach that integrates highly granular air pollution data with a coupled EnergyPlus and differential equation airflow model to evaluate how NV potential for space cooling changes when accounting for air pollution exposure (PM2.5). Given the high vulnerability of low-income populations to air pollution and the dearth of energy and thermal comfort research on informal settlements, we applied our model to a typical informal settlement residence in two large Indian cities: New Delhi and Bangalore. Our results indicate that outdoor PM2.5 levels have a significant impact on NV potential especially in highly polluted cities like New Delhi. However, we found that low-cost filtration (MERV 14) increased the NV potential by 25% and protected occupants from harmful exposure to PM2.5 with a minor energy penalty of 6%. We further find that adoption of low-cost filtration is a viable low-carbon solution pathway as it provides both thermal comfort and exposure protection at 65% less energy intensity—energy intensity reduced to 60 kWh m⁻²from 173.5 kWh m⁻²in case of adoption of potentially unaffordable full mechanical air conditioning. Our work highlights ample opportunities for reducing both air pollution and energy consumption in informal settlements across major Indian cities. Finally, our work can guide building designers and policymakers to reform building codes for adopting low-cost air filtration coupled with NV and subsequently reduce energy demand and associated environmental emissions.

For a variety of environmental, health, and social reasons, there is a pressing need to reduce the automobile dependence of American cities. Bicycles are well suited to help achieve this goal. However, perceptions of rider safety present a large hindrance toward increased bicycle adoption. These perceptions are largely influenced by the design of our current road infrastructure, including the crossing distances of large intersections. In this paper, we examine the role of intersection crossing distances in modifying rider behavior through the construction of a novel dataset integrating street widths and probable trip routes from Chicago’s Divvy bikeshare system. We compare real trips to synthetic trips that are not influenced by the width of intersections and exploit behavior differences that result from the semi-dockless nature of the bikeshare system. Our analysis reveals that bikeshare riders do avoid large intersections in limited circumstances; however, these preferences appear to be heavily outweighed by the relative spatial positions of origins and destinations (i.e., the urban morphology of Chicago). Our results suggest that specific infrastructural investments such as protected intersections could prove feasible alternatives to reduce the perception and safety concerns associated with large road barriers and enhance the attractiveness of non-motorized mobility.